NLM at MEDIQA 2021: Transfer Learning-based Approaches for Consumer Question and Multi-Answer Summarization

Abstract

The quest for seeking health information has swamped the web with consumers' healthrelated questions, which makes the need for efficient and reliable question answering systems more pressing. The consumers' questions, however, are very descriptive and contain several peripheral information (like patient's medical history, demographic information, etc.), that are often not required for answering the question. Furthermore, it contributes to the challenges of understanding natural language questions for automatic answer retrieval. Also, it is crucial to provide the consumers with the exact and relevant answers, rather than the entire pool of answer documents to their question. One of the cardinal tasks in achieving robust consumer health question answering systems is the question summarization and multi-document answer summarization. This paper describes the participation of the U.S. National Library of Medicine (NLM) in Consumer Question and Multi-Answer Summarization tasks of the MEDIQA 2021 challenge at NAACL-BioNLP workshop. In this work, we exploited the capabilities of pre-trained transformer models and introduced a transfer learning approach for the abstractive Question Summarization and extractive Multi-Answer Summarization tasks by first pre-training our model on a task-specific summarization dataset followed by fine-tuning it for both the tasks via incorporating medical entities. We achieved the second, sixth and the fourth position for the Question Summarization task in terms ROUGE-1, ROUGE-2 and ROUGE-L scores respectively.

Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards

“…Ben Abacha et al (2021) organized the MEDIQA-21 shared task challenge on CHQ, multi-document answers, and radiology report summarization. Most of the participating team (Yadav et al, 2021b;He et al, 2021;Sänger et al, 2021) utilized transfer learning, knowledgebased, and ensemble methods to solve the question summarization task. Yadav et al (2021a) proposed question-aware transformer models for question summarization.…”

Question-aware transformer models for consumer health question summarization

“…The majority of the current summarization datasets are either based on the news articles (e.g., CNN/Dailymail [4] arXiv:2206.06581v2 [cs.CL] 15 Jun 2022 and Multi-News [5] datasets where headlines are treated as summaries) or the scientific literature ( e.g., PubMed [6], BioASQ [7] datasets where abstracts of the articles serve as summaries). While significant efforts have been made in the open-domain summarization, there are only a few works [8,9,10,11,12] in summarizing the CHQs. This is partially due to the lack of availability of the human-annotated training datasets.…”